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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 23342

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Dr. Giorgio Leonardi

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Computer Science Institute DiSIT, University of Piemonte Orientale "Amedeo Avogadro", 15121 Alessandria, Italy
Interests: artificial intelligence; biomedical applications of AI; business process management; case based reasoning; temporal abstractions; computational ontologies
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Dr. Enno van der Velde

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Department of Cardiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
Interests: data standardization; medical technology; data exchange; e-health; biomedical informatics; privacy and security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computer science and artificial intelligence play a central role in the healthcare domain from different points of view, e.g., clinical research, decision support, process organization, management and optimization, telemedicine and public health. In this Special Issue, we encourage the submission of original research articles, review articles and short technical communications from the above topics and areas. More specifically, we would like to collect methodological articles with applications and recent developments in the context of artificial intelligence and biomedical informatics applied to the healthcare domain.

Topics include, but are not limited to, the following:

Artificial intelligence;
Data mining/machine learning;
Clinical guidelines;
Decision support and therapy improvement;
Business process management/process mining;
Health data acquisition and analysis;
Healthcare information systems/knowledge representation/reasoning;
Medical imaging and pattern recognition;
Architectures and technologies for telehealth;
Medical signal and data processing.

Dr. Giorgio Leonardi
Dr. Enno van der Velde
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Related Special Issue

Methods, Applications and Developments in Biomedical Informatics: 2nd Edition in Applied Sciences

Published Papers (12 papers)

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Research

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18 pages, 4973 KiB

Open AccessArticle

A Multiuser, Multisite, and Platform-Independent On-the-Cloud Framework for Interactive Immersion in Holographic XR

by Hosein Neeli, Khang Q. Tran, Jose Daniel Velazco-Garcia and Nikolaos V. Tsekos

Appl. Sci. 2024, 14(5), 2070; https://doi.org/10.3390/app14052070 - 01 Mar 2024

Viewed by 608

Abstract

Background: The ever-growing extended reality (XR) technologies offer unique tools for the interactive visualization of images with a direct impact on many fields, from bioinformatics to medicine, as well as education and training. However, the accelerated integration of artificial intelligence (AI) into XR applications poses substantial computational processing demands. Additionally, the intricate technical challenges associated with multilocation and multiuser interactions limit the usability and expansion of XR applications. Methods: A cloud deployable framework (Holo-Cloud) as a virtual server on a public cloud platform was designed and tested. The Holo-Cloud hosts FI3D, an augmented reality (AR) platform that renders and visualizes medical 3D imaging data, e.g., MRI images, on AR head-mounted displays and handheld devices. Holo-Cloud aims to overcome challenges by providing on-demand computational resources for location-independent, synergetic, and interactive human-to-image data immersion. Results: We demonstrated that Holo-Cloud is easy to implement, platform-independent, reliable, and secure. Owing to its scalability, Holo-Cloud can immediately adapt to computational needs, delivering adequate processing power for the hosted AR platforms. Conclusion: Holo-Cloud shows the potential to become a standard platform to facilitate the application of interactive XR in medical diagnosis, bioinformatics, and training by providing a robust platform for XR applications. Full article

(This article belongs to the Special Issue Methods, Applications and Developments in Biomedical Informatics)

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21 pages, 4269 KiB

Open AccessArticle

Brain Extraction Methods in Neonatal Brain MRI and Their Effects on Intracranial Volumes

by Tânia F. Vaz, Nuno Canto Moreira, Lena Hellström-Westas, Nima Naseh, Nuno Matela and Hugo A. Ferreira

Appl. Sci. 2024, 14(4), 1339; https://doi.org/10.3390/app14041339 - 06 Feb 2024

Viewed by 753

Abstract

Magnetic resonance imaging (MRI) plays an important role in assessing early brain development and injury in neonates. When using an automated volumetric analysis, brain tissue segmentation is necessary, preceded by brain extraction (BE) to remove non-brain tissue. BE remains challenging in neonatal brain MRI, and despite the existence of several methods, manual segmentation is still considered the gold standard. Therefore, the purpose of this study was to assess different BE methods in the MRI of preterm neonates and their effects on the estimation of intracranial volumes (ICVs). This study included twenty-two premature neonates (mean gestational age ± standard deviation: 28.4 ± 2.1 weeks) with MRI brain scans acquired at term, without detectable lesions or congenital conditions. Manual segmentation was performed for T2-weighted scans to establish reference brain masks. Four automated BE methods were used: Brain Extraction Tool (BET2); Simple Watershed Scalping (SWS); HD Brain Extraction Tool (HD-BET); and SynthStrip. Regarding segmentation metrics, HD-BET outperformed the other methods with median improvements of +0.031 (BET2), +0.002 (SWS), and +0.011 (SynthStrip) points for the dice coefficient; and −0.786 (BET2), −0.055 (SWS), and −0.124 (SynthStrip) mm for the mean surface distance. Regarding ICVs, SWS and HD-BET provided acceptable levels of agreement with manual segmentation, with mean differences of −1.42% and 2.59%, respectively. Full article

(This article belongs to the Special Issue Methods, Applications and Developments in Biomedical Informatics)

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19 pages, 2760 KiB

Open AccessArticle

The HealthTracker System: App and Cloud-Based Wearable Multi-Sensor Device for Patients Health Tracking

by Cosimo Anglano, Massimo Canonico, Francesco Desimoni, Marco Guazzone and Davide Savarro

Appl. Sci. 2024, 14(2), 887; https://doi.org/10.3390/app14020887 - 19 Jan 2024

Viewed by 1026

Abstract

Telemedicine has emerged as a vital component of contemporary healthcare, revolutionizing the way medical services are delivered and accessed (e.g., it enables patients living in underserved or rural areas to receive medical consultation and treatment remotely). Moreover, telemedicine plays a pivotal role in improving healthcare efficiency by reducing wait times, minimizing unnecessary hospital visits, and optimizing resource allocation. In this paper, we present HealthTracker, a monitoring infrastructure for patients comprising two Internet of Things (IoT) devices (one of which was designed and created by us) and a mobile app that sends data collected by the IoT devices to a cloud service. All these components work together to provide an innovative system able to monitor patient health condition, provide alerts in emergency cases, and elaborate upon data to improve the quality of medical care. Preliminary tests show that the system works well, and real experimentation will start soon in collaboration with the local health authority. Full article

(This article belongs to the Special Issue Methods, Applications and Developments in Biomedical Informatics)

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31 pages, 1504 KiB

Open AccessArticle

Approaches for Dealing with Seasonality in Clinical Prediction Models for Infections

by Bernardo Cánovas-Segura, Antonio Morales, Jose M. Juarez and Manuel Campos

Appl. Sci. 2023, 13(14), 8317; https://doi.org/10.3390/app13148317 - 18 Jul 2023

Viewed by 718

Abstract

The quantitative effect of seasonality on the prevalence of infectious diseases has been widely studied in epidemiological models. However, its influence in clinical prediction models has not been analyzed in great depth. In this work, we study the different approaches that can be employed to deal with seasonality when using white-box models related to infections, including two new proposals based on sliding windows and ensembles. We additionally consider the effects of class imbalance and high dimensionality, as they are common problems that must be confronted when building clinical prediction models. These approaches were tested with four datasets: two created synthetically and two extracted from the MIMIC-III database. Our proposed methods obtained the best results in the majority of the experiments, although traditional approaches attained good results in certain cases. On the whole, our results corroborate the theory that clinical prediction models for infections can be improved by considering the effect of seasonality, although the techniques employed to obtain the best results are highly dependent on both the dataset and the modeling technique considered. Full article

(This article belongs to the Special Issue Methods, Applications and Developments in Biomedical Informatics)

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36 pages, 1342 KiB

Open AccessArticle

META-GLARE: A Computer-Interpretable Guideline System Shell

by Alessio Bottrighi and Paolo Terenziani

Appl. Sci. 2023, 13(14), 8164; https://doi.org/10.3390/app13148164 - 13 Jul 2023

Viewed by 713

Abstract

Computer-interpretable Guideline (CIG) systems are important tools for ensuring healthcare practice quality and standardization. They usually provide a tool to acquire CIGs, and one to execute them on specific patients. Current CIG systems rely on their own formalism to represent clinical guidelines, so moving to new phenomena/domains may require substantial extensions. We propose an innovative approach, providing a “shell” that facilitates system designers to define new CIG systems (or to update an existing one) through the definition of a new CIG representation formalism, based on the Task-Network model. We based it on our previous work on META-GLARE, and we extend it with a general execution tool, able to operate on any CIG representation formalism acquired through the META-GLARE acquisition tool. Developed with modularity and compositionality principles, the tool exploits an open library of basic execution methods. It offers a general execution mechanism supporting various CIG formalisms. We successfully applied our approach to three practical case studies. We have identified a reference CIG formalism (the one currently supported by the META-GLARE library) and compared its expressiveness to benchmark approaches. META-GLARE constitutes the first shell in the literature to facilitate the (formalism-based) design and development of CIG systems, considering both acquisition and execution. Full article

(This article belongs to the Special Issue Methods, Applications and Developments in Biomedical Informatics)

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12 pages, 1374 KiB

Open AccessArticle

Semi-Automated Mapping of German Study Data Concepts to an English Common Data Model

by Anna Chechulina, Jasmin Carus, Philipp Breitfeld, Christopher Gundler, Hanna Hees, Raphael Twerenbold, Stefan Blankenberg, Frank Ückert and Sylvia Nürnberg

Appl. Sci. 2023, 13(14), 8159; https://doi.org/10.3390/app13148159 - 13 Jul 2023

Viewed by 1089

Abstract

The standardization of data from medical studies and hospital information systems to a common data model such as the Observational Medical Outcomes Partnership (OMOP) model can help make large datasets available for analysis using artificial intelligence approaches. Commonly, automatic mapping without intervention from domain experts delivers poor results. Further challenges arise from the need for translation of non-English medical data. Here, we report the establishment of a mapping approach which automatically translates German data variable names into English and suggests OMOP concepts. The approach was set up using study data from the Hamburg City Health Study. It was evaluated against the current standard, refined, and tested on a separate dataset. Furthermore, different types of graphical user interfaces for the selection of suggested OMOP concepts were created and assessed. Compared to the current standard our approach performs slightly better. Its main advantage lies in the automatic processing of German phrases into English OMOP concept suggestions, operating without the need for human intervention. Challenges still lie in the adequate translation of nonstandard expressions, as well as in the resolution of abbreviations into long names. Full article

(This article belongs to the Special Issue Methods, Applications and Developments in Biomedical Informatics)

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24 pages, 5546 KiB

Open AccessArticle

Solution for the Mathematical Modeling and Future Prediction of the COVID-19 Pandemic Dynamics

by Cristina-Maria Stăncioi, Iulia Adina Ștefan, Violeta Briciu, Vlad Mureșan, Iulia Clitan, Mihail Abrudean, Mihaela-Ligia Ungureșan, Radu Miron, Ecaterina Stativă, Michaela Nanu, Adriana Topan, Daniela Oana Toader and Ioana Nanu

Appl. Sci. 2023, 13(13), 7971; https://doi.org/10.3390/app13137971 - 07 Jul 2023

Cited by 1 | Viewed by 897

Abstract

The COVID-19 infectious disease spread in the world represents, by far, one of the most significant moments in humankind’s recent history, affecting daily activities for a long period of time. The data available now allow important modelling developments for the simulation and prediction of the process of an infectious disease spread. The current work provides strong insight for estimation and prediction mathematical model development with emphasis on differentiation between three distinct methods, based on data gathering for Romanian territory. An essential aspect of the research is the quantification and filtering of the collected data. The current work identified five main categories considered as the model’s inputs: inside temperatures (°C), outside temperatures (°C), humidity (%), the number of tests and the quantified value of COVID-19 measures (%) and, as the model’s outputs: the number of new cases, the number of new deaths, the total number of cases or the total number of deaths. Three mathematical models were tested to find the optimal solution: transfer vector models using transfer functions as elements, autoregressive-exogenous (ARX) models, and autoregressive-moving-average (ARMAX) models. The optimal solution was selected by comparing the fit values obtained after the simulation of all proposed models. Moreover, the manuscript includes a study of the complexity of the proposed models. Based on the gathered information, the structure parameters of the proposed models are determined and the validity and the efficiency of the obtained models are proven through simulation. Full article

(This article belongs to the Special Issue Methods, Applications and Developments in Biomedical Informatics)

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Review

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13 pages, 1291 KiB

Open AccessReview

Unveiling Recent Trends in Biomedical Artificial Intelligence Research: Analysis of Top-Cited Papers

by Benjamin S. Glicksberg and Eyal Klang

Appl. Sci. 2024, 14(2), 785; https://doi.org/10.3390/app14020785 - 17 Jan 2024

Viewed by 1383

Abstract

This review analyzes the most influential artificial intelligence (AI) studies in health and life sciences from the past three years, delineating the evolving role of AI in these fields. We identified and analyzed the top 50 cited articles on AI in biomedicine, revealing significant trends and thematic categorizations, including Drug Development, Real-World Clinical Implementation, and Ethical and Regulatory Aspects, among others. Our findings highlight a predominant focus on AIs application in clinical settings, particularly in diagnostics, telemedicine, and medical education, accelerated by the COVID-19 pandemic. The emergence of AlphaFold marked a pivotal moment in protein structure prediction, catalyzing a cascade of related research and signifying a broader shift towards AI-driven approaches in biological research. The review underscores AIs pivotal role in disease subtyping and patient stratification, facilitating a transition towards more personalized medicine strategies. Furthermore, it illustrates AIs impact on biology, particularly in parsing complex genomic and proteomic data, enhancing our capabilities to disentangle complex, interconnected molecular processes. As AI continues to permeate the health and life sciences, balancing its rapid technological advancements with ethical stewardship and regulatory vigilance will be crucial for its sustainable and effective integration into healthcare and research. Full article

(This article belongs to the Special Issue Methods, Applications and Developments in Biomedical Informatics)

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24 pages, 1415 KiB

Open AccessReview

From Seeing to Knowing with Artificial Intelligence: A Scoping Review of Point-of-Care Ultrasound in Low-Resource Settings

by Nethra Venkatayogi, Maanas Gupta, Alaukik Gupta, Shreya Nallaparaju, Nithya Cheemalamarri, Krithika Gilari, Shireen Pathak, Krithik Vishwanath, Carel Soney, Tanisha Bhattacharya, Nirvana Maleki, Saptarshi Purkayastha and Judy Wawira Gichoya

Appl. Sci. 2023, 13(14), 8427; https://doi.org/10.3390/app13148427 - 21 Jul 2023

Cited by 2 | Viewed by 2314

Abstract

The utilization of ultrasound imaging for early visualization has been imperative in disease detection, especially in the first responder setting. Over the past decade, rapid advancements in the underlying technology of ultrasound have allowed for the development of portable point-of-care ultrasounds (POCUS) with handheld devices. The application of POCUS is versatile, as seen by its use in pulmonary, cardiovascular, and neonatal imaging, among many others. However, despite these advances, there is an inherent inability of translating POCUS devices to low-resource settings (LRS). To bridge these gaps, the implementation of artificial intelligence offers an interesting opportunity. Our work reviews recent applications of POCUS devices within LRS from 2016 to 2023, identifying the most commonly utilized clinical applications and areas where further innovation is needed. Furthermore, we pinpoint areas of POCUS technologies that can be improved using state-of-art artificial intelligence technologies, thus enabling the widespread adoption of POCUS devices in low-resource settings. Full article

(This article belongs to the Special Issue Methods, Applications and Developments in Biomedical Informatics)

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16 pages, 1713 KiB

Open AccessReview

Digital Twins: The New Frontier for Personalized Medicine?

by Michaela Cellina, Maurizio Cè, Marco Alì, Giovanni Irmici, Simona Ibba, Elena Caloro, Deborah Fazzini, Giancarlo Oliva and Sergio Papa

Appl. Sci. 2023, 13(13), 7940; https://doi.org/10.3390/app13137940 - 06 Jul 2023

Cited by 7 | Viewed by 5192

Abstract

Digital twins are virtual replicas of physical objects or systems. This new technology is increasingly being adopted in industry to improve the monitoring and efficiency of products and organizations. In healthcare, digital human twins (DHTs) represent virtual copies of patients, including tissues, organs, and physiological processes. Their application has the potential to transform patient care in the direction of increasingly personalized data-driven medicine. The use of DHTs can be integrated with digital twins of healthcare institutions to improve organizational management processes and resource allocation. By modeling the complex multi-omics interactions between genetic and environmental factors, DHTs help monitor disease progression and optimize treatment plans. Through digital simulation, DHT models enable the selection of the most appropriate molecular therapy and accurate 3D representation for precision surgical planning, together with augmented reality tools. Furthermore, they allow for the development of tailored early diagnosis protocols and new targeted drugs. Furthermore, digital twins can facilitate medical training and education. By creating virtual anatomy and physiology models, medical students can practice procedures, enhance their skills, and improve their understanding of the human body. Overall, digital twins have immense potential to revolutionize healthcare, improving patient care and outcomes, reducing costs, and enhancing medical research and education. However, challenges such as data security, data quality, and data interoperability must be addressed before the widespread adoption of digital twins in healthcare. We aim to propose a narrative review on this hot topic to provide an overview of the potential applications of digital twins to improve treatment and diagnostics, but also of the challenges related to their development and widespread diffusion. Full article

(This article belongs to the Special Issue Methods, Applications and Developments in Biomedical Informatics)

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14 pages, 911 KiB

Open AccessReview

Blockchain for the Healthcare Supply Chain: A Systematic Literature Review

by Matteo Fiore, Angelo Capodici, Paola Rucci, Alessandro Bianconi, Giulia Longo, Matteo Ricci, Francesco Sanmarchi and Davide Golinelli

Appl. Sci. 2023, 13(2), 686; https://doi.org/10.3390/app13020686 - 04 Jan 2023

Cited by 14 | Viewed by 5962

Abstract

A supply chain (SC) is a network of interests, information, and materials involved in processes that produce value for customers. The implementation of blockchain technology in SC management in healthcare has had results. This review aims to summarize how blockchain technology has been used to address SC challenges in healthcare, specifically for drugs, medical devices (DMDs), and blood, organs, and tissues (BOTs). A systematic review was conducted by following the PRISMA guidelines and searching the PubMed and Proquest databases. English-language studies were included, while non-primary studies, as well as surveys, were excluded. After full-text assessment, 28 articles met the criteria for inclusion. Of these, 15 (54%) were classified as simulation studies, 12 (43%) were classified as theoretical, and only one was classified as a real case study. Most of the articles (n = 23, 82%) included the adoption of smart contracts. The findings of this systematic review indicated a significant but immature interest in the topic, with diverse ideas and methodologies, but without effective real-life applications. Full article

(This article belongs to the Special Issue Methods, Applications and Developments in Biomedical Informatics)

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Other

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10 pages, 1036 KiB

Open AccessSystematic Review

Machine Learning and miRNAs as Potential Biomarkers of Breast Cancer: A Systematic Review of Classification Methods

by Jorge Alberto Contreras-Rodríguez, Diana Margarita Córdova-Esparza, María Zenaida Saavedra-Leos and Macrina Beatriz Silva-Cázares

Appl. Sci. 2023, 13(14), 8257; https://doi.org/10.3390/app13148257 - 17 Jul 2023

Cited by 1 | Viewed by 1258

Abstract

This work aims to offer an analysis of empirical research on the automatic learning methods used in detecting microRNA (miRNA) as potential markers of breast cancer. To carry out this study, we consulted the sources of Google Scholar, IEEE, PubMed, and Science Direct using appropriate keywords to meet the objective of the research. The selection of interesting articles was carried out using exclusion and inclusion criteria, as well as research questions. The results obtained in the search were 36 articles, of which PubMed = 14, IEEE = 8, Science Direct = 4, Google Scholar = 10; among them, six were selected, since they met the search perspective. In conclusion, we observed that the machine learning methods frequently mentioned in the reviewed studies were Support Vector Machine (SVM) and Random Forest (RF), the latter obtaining the best performance in terms of precision. Full article

(This article belongs to the Special Issue Methods, Applications and Developments in Biomedical Informatics)

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